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              <p class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../_cpp_api/namespace_torch_tensorrt.html">Namespace torch_tensorrt</a></li>
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  <section id="example-notebooks">
<span id="notebooks"></span><h1>Example notebooks<a class="headerlink" href="#example-notebooks" title="Permalink to this headline">¶</a></h1>
<p>There exists a number of notebooks which cover specific using specific features and models
with Torch-TensorRT</p>
<section id="id1">
<h2>Notebooks<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h2>
<section id="compiling-citrinet-with-torch-tensorrt">
<h3>Compiling CitriNet with Torch-TensorRT<a class="headerlink" href="#compiling-citrinet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>Citrinet is an acoustic model used for the speech to text recognition task. It is a version
of QuartzNet that extends ContextNet, utilizing subword encoding (via Word Piece tokenization)
and Squeeze-and-Excitation(SE) mechanism and are therefore smaller than QuartzNet models. CitriNet
models take in audio segments and transcribe them to letter, byte pair, or word piece sequences.</p>
<p>This notebook demonstrates the steps for optimizing a pretrained CitriNet model with Torch-TensorRT,
and running it to test the speedup obtained.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/CitriNet-example.ipynb">Torch-TensorRT Getting Started - CitriNet</a></p></li>
</ul>
</section>
<section id="compiling-efficentnet-with-torch-tensorrt">
<h3>Compiling EfficentNet with Torch-TensorRT<a class="headerlink" href="#compiling-efficentnet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>EfficentNet is a feedforward CNN designed to achieve better performance and accuracy than alternative architectures
by using a “scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient”.</p>
<p>This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
and running it to test the speedup obtained.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/EfficientNet-example.ipynb">Torch-TensorRT Getting Started - EfficientNet-B0</a></p></li>
</ul>
</section>
<section id="masked-language-modeling-mlm-with-hugging-face-bert-transformer-accelerated-by-torch-tensorrt">
<h3>Masked Language Modeling (MLM) with Hugging Face BERT Transformer accelerated by Torch-TensorRT<a class="headerlink" href="#masked-language-modeling-mlm-with-hugging-face-bert-transformer-accelerated-by-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>“BERT is a transformer model pretrained on a large corpus of English data in a self-supervised fashion.
This way, the model learns an inner representation of the English language that can then be used to extract
features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train
a standard classifier using the features produced by the BERT model as inputs.” (<a class="reference external" href="https://huggingface.co/bert-base-uncased">https://huggingface.co/bert-base-uncased</a>)</p>
<p>This notebook demonstrates the steps for optimizing a pretrained EfficentNet model with Torch-TensorRT,
and running it to test the speedup obtained.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Hugging-Face-BERT.ipynb">Masked Language Modeling (MLM) with Hugging Face BERT Transformer</a></p></li>
</ul>
</section>
<section id="serving-a-model-in-c-using-torch-tensorrt">
<h3>Serving a model in C++ using Torch-TensorRT<a class="headerlink" href="#serving-a-model-in-c-using-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>This example shows how you can load a pretrained ResNet-50 model, convert it to a Torch-TensorRT
optimized model (via the Torch-TensorRT Python API), save the model as a torchscript module, and
then finally load and serve the model with the PyTorch C++ API.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-CPP.ipynb">ResNet C++ Serving Example</a></p></li>
</ul>
</section>
<section id="compiling-resnet50-with-torch-tensorrt">
<h3>Compiling ResNet50 with Torch-TensorRT<a class="headerlink" href="#compiling-resnet50-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a
pretrained ResNet-50 network, and running it to test the speedup obtained.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/Resnet50-example.ipynb">Torch-TensorRT Getting Started - ResNet 50</a></p></li>
</ul>
</section>
<section id="using-dynamic-shapes-with-torch-tensorrt">
<h3>Using Dynamic Shapes with Torch-TensorRT<a class="headerlink" href="#using-dynamic-shapes-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>Making use of Dynamic Shaped Tensors in Torch TensorRT is quite simple. Let’s say you are
using the <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.compile(...)</span></code> function  to compile a torchscript module. One
of the args in this function in this function is <code class="docutils literal notranslate"><span class="pre">input</span></code>: which defines an input to a
module in terms of expected shape, data type and tensor format: <code class="docutils literal notranslate"><span class="pre">torch_tensorrt.Input.</span></code></p>
<p>For the purposes of this walkthrough we just need three kwargs: <cite>min_shape</cite>, <cite>opt_shape`</cite> and <cite>max_shape</cite>.</p>
<div class="highlight-py notranslate"><div class="highlight"><pre><span></span><span class="n">torch_tensorrt</span><span class="o">.</span><span class="n">Input</span><span class="p">(</span>
        <span class="n">min_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">opt_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">max_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">1024</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span>
        <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span>
        <span class="nb">format</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">channel_last</span>
    <span class="p">)</span>
<span class="o">...</span>
</pre></div>
</div>
<p>In this example, we are going to use a simple ResNet model to demonstrate the use of the API.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/dynamic-shapes.ipynb">Torch-TensorRT - Using Dynamic Shapes</a></p></li>
</ul>
</section>
<section id="using-the-fx-frontend-with-torch-tensorrt">
<h3>Using the FX Frontend with Torch-TensorRT<a class="headerlink" href="#using-the-fx-frontend-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>The purpose of this example is to demostrate the overall flow of lowering a PyTorch model to TensorRT
conveniently with using FX.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_lower_to_trt.ipynb">Using the FX Frontend with Torch-TensorRT</a></p></li>
</ul>
</section>
<section id="compiling-a-pytorch-model-using-fx-frontend-with-torch-tensorrt">
<h3>Compiling a PyTorch model using FX Frontend with Torch-TensorRT<a class="headerlink" href="#compiling-a-pytorch-model-using-fx-frontend-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>The purpose of this example is to demonstrate the overall flow of lowering a PyTorch
model to TensorRT via FX with existing FX based tooling</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/getting_started_with_fx_path_module.ipynb">Compiling a PyTorch model using FX Frontend with Torch-TensorRT</a></p></li>
</ul>
</section>
<section id="compiling-lenet-with-torch-tensorrt">
<h3>Compiling LeNet with Torch-TensorRT<a class="headerlink" href="#compiling-lenet-with-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a simple LeNet network.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/lenet-getting-started.ipynb">Torch-TensorRT Getting Started - LeNet</a></p></li>
</ul>
</section>
<section id="accelerate-deep-learning-models-using-quantization-in-torch-tensorrt">
<h3>Accelerate Deep Learning Models using Quantization in Torch-TensorRT<a class="headerlink" href="#accelerate-deep-learning-models-using-quantization-in-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>Model Quantization is a popular way of optimization which reduces the size of models thereby
accelerating inference, also opening up the possibilities of deployments on devices with lower
computation power such as Jetson. Simply put, quantization is a process of mapping input values</p>
<blockquote>
<div><p>from a larger set to output values in a smaller set. In this notebook, we illustrate the workflow
that you can adopt while quantizing a deep learning model in Torch-TensorRT. The notebook takes
you through an example of Mobilenetv2 for a classification task on a subset of Imagenet Dataset
called Imagenette which has 10 classes.</p>
</div></blockquote>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/qat-ptq-workflow.ipynb">Accelerate Deep Learning Models using Quantization in Torch-TensorRT</a></p></li>
</ul>
</section>
<section id="object-detection-with-torch-tensorrt-ssd">
<h3>Object Detection with Torch-TensorRT (SSD)<a class="headerlink" href="#object-detection-with-torch-tensorrt-ssd" title="Permalink to this headline">¶</a></h3>
<p>This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained SSD network, and running it to test the speedup obtained.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/ssd-object-detection-demo.ipynb">Object Detection with Torch-TensorRT (SSD)</a></p></li>
</ul>
</section>
<section id="deploying-quantization-aware-trained-models-in-int8-using-torch-tensorrt">
<h3>Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT<a class="headerlink" href="#deploying-quantization-aware-trained-models-in-int8-using-torch-tensorrt" title="Permalink to this headline">¶</a></h3>
<p>Quantization Aware training (QAT) simulates quantization during training by
quantizing weights and activation layers. This will help to reduce the loss in
accuracy when we convert the network trained in FP32 to INT8 for faster inference.
QAT introduces additional nodes in the graph which will be used to learn the dynamic
ranges of weights and activation layers. In this notebook, we illustrate the following
steps from training to inference of a QAT model in Torch-TensorRT.</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/pytorch/TensorRT/blob/master/notebooks/vgg-qat.ipynb">Deploying Quantization Aware Trained models in INT8 using Torch-TensorRT</a></p></li>
</ul>
</section>
</section>
</section>


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